Building an expert system for turbomachinery design requires balancing high physical accuracy with fast iteration speeds.

This webinar demonstrates how Physics-Enhanced Machine Learning (PEML), founded on 3D Inverse Design principles, enables rapid, ultra-accurate performance predictions using small, targeted training datasets.

We will compare PEML against competing AI/ML architectures currently promoted for engineering design, highlighting the specific limitations of pure data-driven models in complex fluid dynamics. Through real-world case studies across multiple flow regimes and working fluids, you will see how PEML delivers verified multi-objective performance gains on standard engineering workstations.